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European Radiology

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Open Access 01-08-2021 | Breast Cancer | Breast

Can artificial intelligence reduce the interval cancer rate in mammography screening?

Authors: Kristina Lång, Solveig Hofvind, Alejandro Rodríguez-Ruiz, Ingvar Andersson

Published in: European Radiology | Issue 8/2021

Abstract

Objectives

To investigate whether artificial intelligence (AI) can reduce interval cancer in mammography screening.

Materials and methods

Preceding screening mammograms of 429 consecutive women diagnosed with interval cancer in Southern Sweden between 2013 and 2017 were analysed with a deep learning–based AI system. The system assigns a risk score from 1 to 10. Two experienced breast radiologists reviewed and classified the cases in consensus as true negative, minimal signs or false negative and assessed whether the AI system correctly localised the cancer. The potential reduction of interval cancer was calculated at different risk score thresholds corresponding to approximately 10%, 4% and 1% recall rates.

Results

A statistically significant correlation between interval cancer classification groups and AI risk score was observed (p < .0001). AI scored one in three (143/429) interval cancer with risk score 10, of which 67% (96/143) were either classified as minimal signs or false negative. Of these, 58% (83/143) were correctly located by AI, and could therefore potentially be detected at screening with the aid of AI, resulting in a 19.3% (95% CI 15.9–23.4) reduction of interval cancer. At 4% and 1% recall thresholds, the reduction of interval cancer was 11.2% (95% CI 8.5–14.5) and 4.7% (95% CI 3.0–7.1). The corresponding reduction of interval cancer with grave outcome (women who died or with stage IV disease) at risk score 10 was 23% (8/35; 95% CI 12–39).

Conclusion

The use of AI in screen reading has the potential to reduce the rate of interval cancer without supplementary screening modalities.

Key Points

• Retrospective study showed that AI detected 19% of interval cancer at the preceding screening exam that in addition showed at least minimal signs of malignancy. Importantly, these were correctly localised by AI, thus obviating supplementary screening modalities.

• AI could potentially reduce a proportion of particularly aggressive interval cancers.

• There was a correlation between AI risk score and interval cancer classified as true negative, minimal signs or false negative.

Ferlay J, Colombet M, Soerjomataram I et al (2018) Cancer incidence and mortality patterns in Europe: estimates for 40 countries and 25 major cancers in 2018. Eur J Cancer 103:356–387. https://doi.org/10.1016/j.ejca.2018.07.005CrossRefPubMed

Houssami N, Hunter K (2017) The epidemiology, radiology and biological characteristics of interval breast cancers in population mammography screening. NPJ Breast Cancer 3:12. https://doi.org/10.1038/s41523-017-0014-xCrossRefPubMedPubMedCentral

Evans KK, Birdwell RL, Wolfe JM (2013) If you don’t find it often, you often don’t find it: why some cancers are missed in breast cancer screening. PLoS One 8:e64366. https://doi.org/10.1371/journal.pone.0064366CrossRefPubMedPubMedCentral

Meshkat B, Prichard RS, Al-Hilli Z et al (2015) A comparison of clinical-pathological characteristics between symptomatic and interval breast cancer. Breast 24:278–282. https://doi.org/10.1016/j.breast.2015.02.032CrossRefPubMed

Perry N, Broeders M, de Wolf C, Tornberg S, Holland R, von Karsa L (2008) European guidelines for quality assurance in breast cancer screening and diagnosis. Fourth edition--summary document. Ann Oncol 19:614–622. https://doi.org/10.1093/annonc/mdm481CrossRefPubMed

Euler-Chelpin MV, Lillholm M, Napolitano G, Vejborg I, Nielsen M, Lynge E (2018) Screening mammography: benefit of double reading by breast density. Breast Cancer Res Treat 171:767–776. https://doi.org/10.1007/s10549-018-4864-1CrossRefPubMedPubMedCentral

Sankatsing VDV, Fracheboud J, de Munck L et al (2018) Detection and interval cancer rates during the transition from screen-film to digital mammography in population-based screening. BMC Cancer 18:256–256. https://doi.org/10.1186/s12885-018-4122-2CrossRefPubMedPubMedCentral

Hofvind S, Skaane P, Vitak B et al (2005) Influence of review design on percentages of missed interval breast cancers: retrospective study of interval cancers in a population-based screening program. Radiology 237:437–443. https://doi.org/10.1148/radiol.2372041174CrossRefPubMed

Houssami N, Irwig L, Ciatto S (2006) Radiological surveillance of interval breast cancers in screening programmes. Lancet Oncol 7:259–265. https://doi.org/10.1016/s1470-2045(06)70617-9CrossRefPubMed

10.

McKinney SM, Sieniek M, Godbole V et al (2020) International evaluation of an AI system for breast cancer screening. Nature 577:89–94. https://doi.org/10.1038/s41586-019-1799-6CrossRefPubMed

11.

Rodriguez-Ruiz A, Lång K, Gubern-Merida A et al (2019) Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J Natl Cancer Inst. https://doi.org/10.1093/jnci/djy222

12.

Schaffter T, Buist DSM, Lee CI et al (2020) Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw Open 3:e200265. https://doi.org/10.1001/jamanetworkopen.2020.0265CrossRefPubMedPubMedCentral

13.

Wu N, Phang J, Park J et al (2020) Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans Med Imaging 39:1184–1194. https://doi.org/10.1109/tmi.2019.2945514CrossRefPubMed

14.

Kim H-E, Kim HH, Han B-K et al (2020) Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit Health 2:e138–e148. https://doi.org/10.1016/S2589-7500(20)30003-0CrossRefPubMed

15.

Wu N, Phang J, Park J et al (2019) Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2019.2945514:1-1

16.

Rodríguez-Ruiz A, Krupinski E, Mordang J-J et al (2018) Detection of breast cancer with mammography: effect of an artificial intelligence support system. Radiology 290:305–314. https://doi.org/10.1148/radiol.2018181371CrossRefPubMed

17.

Rodriguez-Ruiz A, Lång K, Gubern-Merida A et al (2019) Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. Eur Radiol. https://doi.org/10.1007/s00330-019-06186-9

18.

Yala A, Schuster T, Miles R, Barzilay R, Lehman C (2019) A deep learning model to triage screening mammograms: a simulation study. Radiology 293:38–46. https://doi.org/10.1148/radiol.2019182908CrossRefPubMed

19.

Kyono T, Gilbert FJ, van der Schaar M (2020) Improving workflow efficiency for mammography using machine learning. J Am Coll Radiol 17:56–63. https://doi.org/10.1016/j.jacr.2019.05.012CrossRefPubMed

20.

Lång K, Dustler M, Dahlblom V, Åkesson A, Andersson I, Zackrisson S (2020) Identifying normal mammograms in a large screening population using artificial intelligence. Eur Radiol. https://doi.org/10.1007/s00330-020-07165-1

21.

Mordang J-J, Janssen T, Bria A, Kooi T, Gubern-Mérida A, Karssemeijer N (2016) Automatic microcalcification detection in multi-vendor mammography using convolutional neural networks. In: Tingberg A, Lång K, Timberg P (eds) Breast imaging. Springer International Publishing, Cham, pp 35–42CrossRef

22.

Bria A, Karssemeijer N, Tortorella F (2014) Learning from unbalanced data: a cascade-based approach for detecting clustered microcalcifications. Med Image Anal 18:241–252. https://doi.org/10.1016/j.media.2013.10.014CrossRefPubMed

23.

Kooi T, Litjens G, van Ginneken B et al (2017) Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 35:303–312. https://doi.org/10.1016/j.media.2016.07.007CrossRef

24.

Cornford E, Sharma N (2019) Interval cancers and duty of candour, a UK perspective. Curr Breast Cancer Reports 11:89–93. https://doi.org/10.1007/s12609-019-0304-4CrossRef

25.

Ciatto S, Visioli C, Paci E, Zappa M (2004) Breast density as a determinant of interval cancer at mammographic screening. Br J Cancer 90:393–396. https://doi.org/10.1038/sj.bjc.6601548CrossRefPubMedPubMedCentral

26.

Hovda T, Holen ÅS, Lång K et al (2019) Interval and consecutive round breast cancer after digital breast tomosynthesis and synthetic 2D mammography versus standard 2D digital mammography in BreastScreen Norway. Radiology 294:256–264. https://doi.org/10.1148/radiol.2019191337CrossRefPubMed

27.

Houssami N, Bernardi D, Caumo F et al (2018) Interval breast cancers in the ‘screening with tomosynthesis or standard mammography’ (STORM) population-based trial. Breast 38:150–153. https://doi.org/10.1016/j.breast.2018.01.002CrossRefPubMed

28.

Bakker MF, de Lange SV, Pijnappel RM et al (2019) Supplemental MRI screening for women with extremely dense breast tissue. N Engl J Med 381:2091–2102. https://doi.org/10.1056/NEJMoa1903986CrossRefPubMed

29.

Ohuchi N, Suzuki A, Sobue T et al (2016) Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the Japan Strategic Anti-cancer Randomized Trial (J-START): a randomised controlled trial. Lancet 387:341–348. https://doi.org/10.1016/s0140-6736(15)00774-6CrossRefPubMed

30.

Christiana B, Alejandro R-R, Christoph M, Nico K, Sylvia HH-K (2020) Going from double to single reading for screening exams labeled as likely normal by AI: what is the impact?, Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020) 115130D. https://doi.org/10.1117/12.2564179

31.

Nishikawa RM, Schmidt RA, Linver MN, Edwards AV, Papaioannou J, Stull MA (2012) Clinically missed cancer: how effectively can radiologists use computer-aided detection? AJR Am J Roentgenol 198:708–716. https://doi.org/10.2214/ajr.11.6423CrossRefPubMed

32.

Ciatto S, Houssami N, Ambrogetti D, Bonardi R, Collini G, Del Turco MR (2007) Minority report - false negative breast assessment in women recalled for suspicious screening mammography: imaging and pathological features, and associated delay in diagnosis. Breast Cancer Res Treat 105:37–43. https://doi.org/10.1007/s10549-006-9425-3CrossRefPubMed

33.

Lameijer JRC, Voogd AC, Pijnappel RM et al (2020) Delayed breast cancer diagnosis after repeated recall at biennial screening mammography: an observational follow-up study from the Netherlands. Br J Cancer 123:325–332. https://doi.org/10.1038/s41416-020-0870-2CrossRefPubMedPubMedCentral

Title: Can artificial intelligence reduce the interval cancer rate in mammography screening?
Authors: Kristina Lång
Solveig Hofvind
Alejandro Rodríguez-Ruiz
Ingvar Andersson
Publication date: 01-08-2021
Publisher: Springer Berlin Heidelberg
Keywords: Breast Cancer
Mammography
Artificial Intelligence
Breast Cancer
Mammography
Published in: European Radiology / Issue 8/2021
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-021-07686-3

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Can artificial intelligence reduce the interval cancer rate in mammography screening?

Abstract

Objectives

Materials and methods

Results

Conclusion

Key Points

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Objectives

Materials and methods

Results

Conclusion

Key Points

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